Resource Allocation Of 5G Network By Particle Swarm Optimization And Game Theory

Given the high use of mobile communications,it is more recommended to deploy cellular networks whose diagrams allow the reintegration of frequencies.In 5G,the challenge is to increase the capacity and the throughput for a multitude of radiant base stations(BS)at minimum power in an urban area.Nevertheless,the coefficient(ICI)caused by the spectral overlap between base stations.ICI decreases network capacity,causing serious problems in the performance of User Equipment(UE).Multiuser OFDMA networks include LTE/LTE-A networks and dense small-scale cell networks,the issues regarding the assignment of resource and power control discussed in this thesis.The contribution of this thesis derived into the following two parts: In the first part,we propose the resource allocation algorithm based on particle swarm optimization(PSO)in the 5G network.To minimize the computation costs,an objective function is formulated with some constraints,which denote the users' Qo S requirements and limits.For solving the issue regarding composite nonlinear optimization,a unique population-built search algorithm is employed is referred to as the PSO.A PSO algorithm is presented to search the maximum allocation in each cell and corresponding resource allocation.In the second part,we propose the power control of the 5G network based on Game theory(GT).The GT,used for power control optimization of the 5G network.The issue of power allotment is resolved by employing a decentralized GT procedure and the requirement of signaling messages interchange among distinct cells is eliminated.The resource and power are assigned to the group of nearby cells collectively.The required information is received from the cells in the neighborhood after which the transmission power is altered to achieve the desired power.In power control,a nondeterministic method called the stochastic algorithm can also be used.The algorithm simulated for a total of 10 base stations in three scenarios.However,the results for GT are compared throughput with a stochastic algorithm and it is proven more reliable in case of power control.